Why data science is the inevitable next step

During one of my late nights at the office, I looked over the latest business rankings. I tried to spot which companies in the Fortune and Forbes lists had been in the headlines of late for their investments in data science.

We had been hard at work over the past few months on matters related to AIM’s newest degree program, the Master of Science in Data Science. Naturally, it was foremost in my thoughts.

Top 10 rankings for the so-called “unicorns” (billion-dollar tech startups) include the likes of Uber, Airbnb, Pinterest, India’s dominant e-commerce site Flipkart, and data analytics service provider Palantir. Chinese ride-sharing app Didi Chuxing (with a valuation of US$50 billion) follows close on the heels of the world’s most valuable private company at the moment, Uber (US$68 billion).

As expected, most if not all the unicorns were built on the back of data science. But how about the top 10 companies in the Fortune 500, organizational behemoths founded in the last century? Well, they too had made headlines because of large investments in data science and analytics. Take for example the number one on the list for five years running, Walmart.

How can Walmart still operate 20,000 stores in 28 countries in the era of online retail? It is not only the golden age of Amazon, after all, but also that of thousands of not-so-insignificant online stores and the long tail of e-commerce websites. How has the retail giant stayed afloat? The answer is data science.

Half a century into the game, Walmart decided to invest in what was touted by headlines at the start of 2017 as the biggest private cloud on Earth. The reason? It can crunch through 2.5 petabytes worth of data per hour to gain insights. From my many chats with the data scientists at AIM, I learned that 2.5 petabytes is just shy of 625,000 high-definition movies (about 4 gigabytes each)!

So how do data scientists help Walmart stay relevant in the Amazon Era? In a nutshell, they solve problems. Any unit in the organization can bring their problem to Walmart’s data scientists, who analyze recent data to root out the cause of the issue, and then present the unit with a logical solution—in minutes.

In an interview with Forbes, Walmart’s Senior Statistical Analyst Naveen Peddamail said, “If you can’t get insights until you’ve analyzed your sales for a week or a month, then you’ve lost sales within that time. If you can cut down that time from two or three weeks to 20 or 30 minutes, then that saves a lot of money for Walmart and stopped us losing sales.”

Apparently, problem-to-resolution takes place in rapid-fire fashion at Walmart thanks to data science. Why did a product stop selling all of a sudden? Because a miscalculation caused it to be listed at a higher price point in certain regions. Why is a popular product selling in all stores but two? Someone simply failed to stock the product on the shelves.

The inverse is a scary prospect for any business that wants to stay competitive beyond 2017. If you happen to be an organization operating without the benefit of data science, it is anyone’s guess how long it will take you to figure out any cause of declining revenue.

Going down the list of Fortune 500 companies, you would be hard-pressed to find an organization that is not currently hiring data scientists. From healthcare companies to automobile manufacturers, all see the value of mining, refining, and making work for them what is widely regarded as the new gold, the new oil.

Everyone is in the race to hire data scientists who not only know how to harvest data but can also model, visualize, and turn it into actionable insights that could not have been brought to light by anyone else. An equally important competency is the ability to pose the right questions that matter to the business.

Talk to Ford and they will tell you how using text-mining algorithms on social media posts saved the company by providing consumer insights not available via traditional market research.

General Motors, for its part, recently released a paper on how their data Scientists helped create a global-parts pricing strategy to stay competitive in the US$2-trillion-per-annum spare parts business. GM is one of the largest OEMs in the world and, as one manager put it, “needed a medium-term solution, not a 10-year plan.”

Both carmakers are in the Top 10 of the Fortune 500, which represents two-thirds of the US GDP; but what of Asia?

Take a look at the Asian rankings and you see the same thing happening with the likes of China’s online retailing giant Alibaba and search engine Baidu; as well as India’s Bajaj Finserv, whose managing director said they would explore investing in fintech (financial technology), which has data science as its backbone.

Fintech innovates on the delivery of traditional financial services through, for instance, smartphones to perform a variety of tasks, from securing loans to managing investment portfolios.

Harvard Business Review may have called the profession of data scientist the “sexiest job of the 21st century,” but I think “most lucrative” may be an even more apt description.

AIM has already received an unprecedented number of inquiries about the Master of Science in Data Science program from several quarters who understand the urgency of deepening our country’s talent pool; and from would-be students who realize it is the natural, inevitable, next step.

Our job now as a business school is to make sure the data science training we offer ties in strongly with Business and Management. As our data scientists—professionals with successful track records in the region—often say, they have created a program in which they themselves would wish to take part.

Dr. Jikyeong Kang is President and Dean of the Asian Institute of Management. For more information, e-mail JKangMT@AIM.edu or visit AIM.edu.